Segmentation of MR Images of the Human Brain Using Fuzzy Adaptive Radial Basis Function Neural Network
A method for segmentation of magnetic resonance (MR) images of the human brain using a fuzzy adaptive radial basis function neural network (FARBF-NN) has been proposed. Since the quality of MR images always gets affected by intensity in-homogeneities (artifacts or noises), generated due to the non-uniformity of magnetic fields during the acquisition process, thereby making segmentation task more difficult. The outputs of the hidden layer neurons of the FARBF-NN have been modified using a fuzzy membership function to eliminate the effect of noises present in the input image. The proposed method has been tested both on simulated and real patient MR brain images for segmentation and found to be better than the k-means clustering algorithm, the fuzzy c-means (FCM) clustering algorithm, and the RBF neural network that uses k-means clustering algorithm to select the centers of the RBFs in the hidden layer, in most of the cases.
- 1.Clarke, L.P., et al.: MRI Segmentation: Methods and applications. Magn. Reson. Imag., 343–368 (1995)Google Scholar
- 2.Sing, J.K., Basu, D.K., Nasipuri, M., Kundu, M.: Center Selection of RBF Neural Network Based on Modified K-Means Algorithm With Point Symmetry Distance Measure. Foundation of Computing and Decision Sciences, 247–266 (2004) Google Scholar